Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees

TL;DR

Adv-SSL introduces an adversarial self-supervised learning method with theoretical guarantees that produces unbiased, well-clustered representations, leading to improved transfer learning and few-shot classification performance.

Contribution

It proposes a novel adversarial self-supervised learning framework with theoretical analysis, addressing bias issues in existing covariance-based methods without additional computational costs.

Findings

Outperforms existing methods on multiple benchmarks

Provides theoretical guarantees for representation quality

Enhances few-shot learning performance

Abstract

Learning transferable data representations from abundant unlabeled data remains a central challenge in machine learning. Although numerous self-supervised learning methods have been proposed to address this challenge, a significant class of these approaches aligns the covariance or correlation matrix with the identity matrix. Despite impressive performance across various downstream tasks, these methods often suffer from biased sample risk, leading to substantial optimization shifts in mini-batch settings and complicating theoretical analysis. In this paper, we introduce a novel \underline{\bf Adv}ersarial \underline{\bf S}elf-\underline{\bf S}upervised Representation \underline{\bf L}earning (Adv-SSL) for unbiased transfer learning with no additional cost compared to its biased counterparts. Our approach not only outperforms the existing methods across multiple benchmark datasets but is also supported by comprehensive end-to-end theoretical guarantees. Our analysis reveals that the minimax optimization in Adv-SSL encourages representations to form well-separated clusters in the embedding space, provided there is sufficient upstream unlabeled data. As a result, our method achieves strong classification performance even with limited downstream labels, shedding new light on few-shot learning.